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. 2022 Oct 6;12:16682. doi: 10.1038/s41598-022-20348-8

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© The Author(s) 2022, corrected publication 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Algorithm development. Raw data from voice signals were preprocessed after normalization. Clinical data were concatenated to the Praat features in the machine learning models. A two step-process was then used to first classify those with oral feeding versus tube feeding (algorithm 1) and, among the latter, classify those at high risk of respiratory complications (algorithm 2). ML machine learning, SVM support vector machine, GMM Gaussian mixture model, XGBoost extreme gradient boosting.